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Earth and Environmental Sciences - Sophomore

Course # EAES 2011

Credits 6

Prerequisites and/or Corequisites:  Precalculus, Calculus

Course Description 

This course introduces students to physical processes and ways of thinking quantitatively about the world around us, to understand every day and specific physical phenomena related to Earth and Environmental Sciences. The course includes introductions to mechanics and gravity (how objects move, potential field gravity field, principles of Newtonian mechanics, stress and strain), fluids and material properties (how solids, liquids and gases behave, buoyancy forces, gases rules, model of ideal gases, thermal physics (how heat moves, latent constant, thermodynamics laws), and waves (e.g., light and sound). Throughout the course, we’ll develop skills of asking physics questions and making scientific estimates. 

Course learning outcomes 

Upon completion of this course, the students will be able to:

  • Apply kinematic equations to non-accelerating frames.
  • recognize fundamental concepts throughout physics (e.g., conservation of energy).
  • recognize that EES phenomena as combination of multiple physical processes
  • interpret physical concepts quantitatively
  • derive the most important physics concepts and equations for EES

Course Assessments and Grading  

Item

Weight

Homework

10%

Quizzes

15%

Laboratory experiments

10%

Project

15%

Midterm Exam

20%

Final exam

30%

Course # EAES 3001

Credits 6

Prerequisites and/or Corequisites:  Information Technology course

Course Description

This course provides a theoretical and practical introduction to the fundamental principles of
Geographic Information Systems (GIS) and Remote Sensing digital image processing. It is focused
on the essential skills of operating a functional GIS with ArcGIS Pro software package, which is
one of the most widely used desktop GIS applications in the world. This course analyses generic
programming language concepts and techniques and demonstrates their implementation using
Python in GIS. The fundamental principles and methods of introductory and intermediate
geographic information science are explored as students practice ways to think spatially and
develop ways to work with and apply new GIS knowledge to real world problems.

Course Learning Outcomes

Upon completion of this course, the students will be able to:

  • Explain the main concepts that define Geographic Information Systems
  • Explain how and why geographic data are entered, stored, and manipulated using GIS, and how to acquire, process, and analyze remotely sensed data.
  • Conduct basic spatial analyses including clip analysis, slope analysis, and IDW tools.
  • Explain how to properly use geospatial analysis for a wide range of applications, such as ESRI's ArcGIS software.
  • Analyze spatial data, using GIS analysis tools such as Network and Buffer Analysis
  • Apply Python programming language as a GIS computer language and using the special ‘arcpy’ package.
  • Apply modern GIS and Remote Sensing Technologies like Raster Calculation, Map Algebra, Raster Vector Conversions, Surface Analysis, reclassified a slope raster etc.
  • Course Assessment and Grading

Item

Weight

6 Home Assignments

60%

Class attendance and participation

10%

Final Project

30%

Course # EAES 2046

Credits 6

Prerequisites and/or Co-requisites: Introduction to Earth and Environmental Sciences

Course Description

Geomorphology examines the Earth's surface and is essential for addressing various environmental and engineering challenges. It has even become an important tool for understanding how far-off planets like Mars and Venus have evolved to their current state.  This course is about earth’s landscape, its present form, and the processes responsible for its large-scale organization.  Students will gain insights into the formation and ongoing transformation of their surroundings, with a special emphasis on the mountainous regions of Central Asia. 

Course Learning Outcomes

Upon the completion of the course, students will be able to:

  • Explain principal terms, definitions and theories of geomorphology.
  • Describe landforms and land forming processes in different climate zones and tectonic regimes.
  • Explain different theories and models for landscape evolution.
  • Discuss the development of micro to mega scale landforms and their lifespans
  • Assess the mode of formation, age and history for landforms in mountain environments of Central Asia
  • Compare the formation of large-scale landforms involving both exogenous and endogenous processes

Course Assessments and Grading

Item

Weight

Participation and in-class activities

10%

Lab Assignments

(5 in total)

25%

Fieldwork reports

(2 in total)

20 %

Mid-term Exam

15%

Final Exam

30%

Course # EAES 2030

Credits 6  

Prerequisites and/or Corequisites: Introduction to Earth and Environmental Sciences

Course Description

This course provides hands-on training to use geospatial and environmental facts to produce useful suggestions for improving decisions about location, atmosphere, and climate, from the most natural to the most urban. Students learn how to use geographic information systems and remote sensing to solve challenging issues related to maintaining ecosystems and promoting sustainable practices and to recognize complex environmental issues such as natural resource mismanagement and atmospheric and climate change. Students analyze a wide range of information, including real-time data, mapping, monitoring, management, and live forecast. This course demonstrates how to use remote sensing and geospatial information for many planned and daily decisions across a wide range of sectors, and especially in atmospheric and climate science.  

Course Learning Outcomes

Upon the completion, students will be able to:

  • Use geoscience techniques and current environmental information to define issues and solutions in atmospheric and climate science.
  • Use numerical models and their role in atmospheric and climate science based on current observations to solve earth and environmental problems.
  • Analyze, interpret, process and use climate data to visualize various atmospheric and climate phenomena.
  • Acquire satellite images, perform parameter retrieval, use GIS data for atmospheric forecast and climatology applications.
  • Manage the design, documentation, and resourcing of RS/GIS science solutions in a variety of settings, including workplaces and contested environmental issues.
  • Analyze the atmospheric and climate change impacts, preparedness, response, related policies, law and future plans.
  • Course Assessments and Grading

Item

Weight

Class performance & activities

5%

Lab assignments

5%

Data collection, analysis & reports

15%

Short field work & report

5%

Mid-term exam

20%

Group project & presentation

15%

Workshop Quiz & paper

10%

Final exam

25%

Course # HUSS 2053E

Credits 3

Prerequisites and/or Corequisites: NA

TBA

Course # DMNS 2012E

Credits 3

Course Description

Linear Algebra is a foundational course at UCA. It can be applied in business, economics, sociology, ecology, demography, engineering and other areas.

In this course, students will study mathematics that deals with the system of linear equations and their applications, operations with matrices, applications of Markov chains, applications of determinants, eigenvalues and eigenvectors and their applications. 

Course Learning Outcomes

Upon the completion of this course, students should be able to:

  • Set up and solve a system of equations to fit a polynomial function to a set of data points.
    Use matrices and Gaussian and Gauss – Jordan eliminations to solve a system of linear equations.
  • Do operations with matrices.
  • Find the inverse of a matrix.
    Use a stochastic matrix to find the nth  state matrix of a Markov chain.
    Find steady state matrices of absorbing Markov chain.
    Use matrix algebra to analyze an economic system (Leontief input- output model).
    Find the least square regressions line for a set of data.
    Use Cramer’s rules to solve a system of n linear equations in n variables.
    Model population growth using an age transition matrix and an age distribution vector.
    Solve Linear Algebra problems wit the application of R studio.

Course Assessments and Grading

Item

Weight

Test 1 

a) paper based test;

b) computer (R studio) based test.

 

15 %

10 %

Attendance

5 %

Test 2

a) paper based test;

b) computer (R studio) based test.

 

15 %

10 %

Test on independent work

15 %

Final exam

30 %

Course # EAES 2130E

Credits 3

TBA